Spatiotemporal variation of the nutrients and heavy metals in mangroves using multivariate statistical analysis, Gulf of Kachchh (India)

Maurya, Parul; Kumari, Richa

doi:10.1016/j.envres.2021.110803

Cited by 23 publications

(4 citation statements)

References 43 publications

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“…The common forms of nitrogen are NH4+ and NO3- (Reef et al 2010;Abbasi et al 2017;Alongi 2021), which showed strong correlations in the polyhaline zone, but the denitrification activity and N assimilation decrease with an increase in seawater salinity (Shiau et al 2017;Wang et al 2018). Anthropogenic sources (such as SO4, NO2-N, and PO4) in the mesohaline zone probably originate from fertilizer waste from farming activities, fish ponds, or industries upstream (as for Wu et al 2017;Maurya and Kumari 2021).…”

Section: Pca Analysis For Edaphic Factors and Water Qualitymentioning

confidence: 99%

“…The studies partially correlate the vegetation with edaphic factors or water quality; in fact, the characteristics should show reciprocal multivariate relationships (Cooray et al 2021). Edaphic factors affect the ecophysiology, composition, and structure of mangrove species (Hossain and Nuruddin 2016), while the water quality is able to identify the existence of anthropogenic resources in the ecosystem (Maurya and Kumari 2021).…”

Section: Introductionmentioning

confidence: 99%

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Interspecific associations of mangrove species and their preferences for edaphic factors and water quality

et al. 2022

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Abstract. Marpaung BA, Budiadi, Pertiwiningrum A, Lestari LD, Nurjanto HH, Widiyatno. 2022. Interspecific associations of mangrove species and their preferences for edaphic factors and water quality. Biodiversitas 23: 4626-4635. Scientists from different regions have reported failures of mangrove restorations that were predicted due to the unsuitability of the species for the habitats. Habitat preferences should become an important consideration for mangrove restoration and management. This study aimed to find interspecific associations between species and examine the edaphic factors and water quality suitable for mangroves. Eight research stations (and 24 sample plots) were established purposively based on the vegetation structure and salinity levels in successful mangrove restorations in East Lampung in 2020. In each plot, vegetal data and sediment and water samples were taken. The species domination was analyzed using the important value index (IVI), and interspecific associations were determined using a 2x2 contingency table. The edaphic factors and water quality were analyzed using principal component analysis (PCA) to find factors that had significant correlations among others. The effects of the edaphic factors and water quality on species regeneration were analyzed using multiple linear regression by backward elimination to develop a model of habitat preferences for mangroves. Based on the results, Avicennia marina and Rhizophora sp. were the dominant species in all the zones. Still, the lack of interspecific association ( ) showed that both species had different preferences for the habitat. We found four principal components (PCs) of edaphic factors—i.e., the environmental (soil and surface) temperature, dusty clay and Ca, inorganic nitrogen (NH4-N and NO3-N), and Mg—and two PCs of water quality, i.e., nontoxic pollutants (total suspended solids and turbidity) and anthropogenic factors (NO2-N and PO4-P), as predictors for multiple linear regression. The seedlings of A. marina had no specific preferences for edaphic factors but significantly preferred nontoxic pollutants (R2 = 0.619), while the seedlings of Rhizophora spp. significantly preferred a specific environmental temperature, dusty clay and Ca, and inorganic nitrogen (R2 = 0.768), and nontoxic pollutants (R2 = 0.755). Combining A. marina and Rhizophora spp. in mangrove restorations should be avoided. Therefore, spatial planting patterns or sequential planting based on the succession stages of the habitat could be established.

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Section: Pca Analysis For Edaphic Factors and Water Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interspecific associations of mangrove species and their preferences for edaphic factors and water quality

et al. 2022

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“…Phytoplankton diversity in traditional ponds was 0.119-0.159 ind/L, while in semi-intensive ponds was 0.121-0.159 ind/L. Two factors affecting the quality of nutrients in the water are the availability of mangrove forests at the farming sites (Musa et al 2020;Maurya and Kumari 2021) and water contamination (Jonah and Etteokon 2020).…”

Section: Compositionmentioning

confidence: 99%

Diversity of phytoplankton in the whiteleg (Litopenaeus vannamei) shrimp ponds in the south coastal area of Pangandaran, Indonesia

Palupi

Fitriadi²,

WIJAYA³

et al. 2021

Biodiversitas

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Abstract. Palupi M, Fitriadi R, Wijaya R, Raharjo P, Nurwahyuni R. 2021. Diversity of phytoplankton in the whiteleg (Litopenaeus vannamei) shrimp ponds in the south coastal area of ??Pangandaran, Indonesia. Biodiversitas 23: 118-124. Intensive shrimp farming in ponds is characterized by high stocking densities, complete feeding in quality and quantity, and good environmental control to produce the maximum quantity of shrimp in a fixed area. Phytoplankton is a bioindicator affecting the productivity of whiteleg shrimp in ponds. Currently, intensive shrimp farming activity is carried out on the south coast of Java, which utilizes sandy soil with a biofloc system, but the production has not been optimal. The objective of this study was to analyze the diversity and abundance of phytoplankton and water quality in the traditional and intensive whiteleg shrimp ponds in Pangandaran, West Java, to support sustainable pond management. The objective of the study was achieved by calculating the abundance, diversity index, evenness index and dominance index of planktons in intensive and traditional ponds. The study results showed that Chrysophyta division had the highest abundance and species number in both intensive and traditional ponds. The most abundant phytoplankton found in the intensive ponds was Halosphaera viridis (3143.88 ind/m3), and that in the traditional ponds was Rhizosolenia stolterfothi (1414.746 ind/m3). The phytoplankton diversity index (H') in the intensive ponds was 1.95 and that in the traditional ponds was 2.17. The dominance index value in the intensive ponds was 0.18 and in the traditional ponds 0.15. The abundance of phytoplankton is a limitation of the production success of whiteleg shrimp farming.

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“…[1][2][3][4][5][6][7] In particular, textile industries cause more severe damage to the environment because they use harmful chemicals such as organic pollutants, heavy metals, cationic dyes, anionic dyes, azo dyes, pesticides, and oils. [8][9][10][11][12][13][14][15] Among them, Methylene Blue (MB) is a common cationic dye that is toxic and carcinogenic and is especially used in food processing, pharmaceutics, printing, and textile industries. It has a molecular formula of C 16 H 18 ClN 3 S, is odorless and is a dark green-colored powder but when added to water it appears as a blue solution at room temperature.…”

Section: Introductionmentioning

confidence: 99%

2D/2D nitrogen-doped graphitic carbon nitride/cobalt sulfide nanostructures for fast photodegradation of methylene blue dye and real industrial sewage effluents

Vuggili

Gaur²,

Tyagi

et al. 2023

Environ. Sci.: Adv.

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Spatiotemporal variation of the nutrients and heavy metals in mangroves using multivariate statistical analysis, Gulf of Kachchh (India)

Cited by 23 publications

References 43 publications

Interspecific associations of mangrove species and their preferences for edaphic factors and water quality

Interspecific associations of mangrove species and their preferences for edaphic factors and water quality

Diversity of phytoplankton in the whiteleg (Litopenaeus vannamei) shrimp ponds in the south coastal area of Pangandaran, Indonesia

2D/2D nitrogen-doped graphitic carbon nitride/cobalt sulfide nanostructures for fast photodegradation of methylene blue dye and real industrial sewage effluents

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